{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "275ba2a2",
   "metadata": {},
   "source": [
    "# DAY 55"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1332ea31",
   "metadata": {},
   "source": [
    "在进入rnn相关变体的内容前，我们必须要搞懂序列任务的前生今世，这是我当初自学的时候非常迷茫和痛苦的，只有理解了序列任务，才知道模型为什么这么选择，数据为什么这么处理"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "548e2481",
   "metadata": {},
   "source": [
    "## 一、序列预测任务介绍"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b5ad639",
   "metadata": {},
   "source": [
    "### 1.1 序列预测是什么？"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "187a2875",
   "metadata": {},
   "source": [
    "我们之前接触到的结构化数据，它本身不具备顺序，我们认为每个样本之间独立无关，样本之间即使调换顺序，仍然不影响模型的训练。\n",
    "\n",
    "但是日常中很多数据是存在先后关系的，而他们对应的任务是预测下一步的值，我们把这个任务称之为序列预测。\n",
    "\n",
    "举个例子，比如有过去30天的股票价格，我们希望预测第31天的价格。比如之前的单车预测，有前60天的单车需求数据，希望预测后面20天的销量。或者文本，人的语言是有顺序的，预测下一个单词是这也是序列预测任务。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74af282f",
   "metadata": {},
   "source": [
    "### 1.2 序列预测的x-y对"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "383d494d",
   "metadata": {},
   "source": [
    "那么如何把之前结构化的数据，转化为序列的样子呢？\n",
    "\n",
    "答案就是通过滑动窗口这个方式来实现。序列任务也需要有自己的数据对，这样才符合监督学习的训练。把原始数据序列，转化为x-y这样的标签对，实现用前seq_length个时间步预测下一个时间步的监督学习格式。\n",
    "\n",
    "假设原始数据 data = [10, 20, 30, 40, 50, 60, 70, 80, 90]，\n",
    "\n",
    "序列长度 seq_length = 3\n",
    "\n",
    "这个x-y序列对如下\n",
    "\n",
    "```python\n",
    "X = [\n",
    "  [10, 20, 30],  # 用前3步预测第4步\n",
    "  [20, 30, 40],  # 用2-4步预测第5步\n",
    "  [30, 40, 50],  # 用3-5步预测第6步\n",
    "  [40, 50, 60],  # 用4-6步预测第7步\n",
    "  [50, 60, 70],  # 用5-7步预测第8步\n",
    "  [60, 70, 80]   # 用6-8步预测第9步\n",
    "]\n",
    "\n",
    "y = [40, 50, 60, 70, 80, 90]  # 每个X对应的下一个值\n",
    "```\n",
    "\n",
    "其中第一个x-y对是[10, 20, 30]-->[40]，第二个x-y对是[20, 30, 40]-->[50]，第三个x-y对是[30, 40, 50]-->[60]，第四个x-y对是[40, 50, 60]-->[70]，第五个x-y对是[50, 60, 70]-->[80]，第六个x-y对是[60, 70, 80]-->[90]。这个样本对，和我们之前结构化数据见到的样本-标签对 是一个逻辑。\n",
    "\n",
    "\n",
    "注意，最后三个值 [70, 80, 90] 不能作为输入（因为没有后续值作为目标），所以生成的样本数为 len(data) - seq_length = 9 - 3 = 6\n",
    "```python\n",
    "可以把上述过程理解为一个尺寸为3的窗口在滑动的过程（类似于卷积核滑动），\n",
    "\n",
    "滑动窗口过程:\n",
    "[10, 20, 30] → 40\n",
    "   [20, 30, 40] → 50\n",
    "      [30, 40, 50] → 60\n",
    "         [40, 50, 60] → 70\n",
    "            [50, 60, 70] → 80\n",
    "               [60, 70, 80] → 90\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41523191",
   "metadata": {},
   "source": [
    "### 1.3 序列预测的标准输入"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47c665be",
   "metadata": {},
   "source": [
    "一个标准的序列数据张量通常具有三个维度：[批量大小, 序列长度, 特征维度]\n",
    "\n",
    "批量大小 (Batch Size)：一次性喂给模型多少个独立的样本进行处理\n",
    "序列长度 (Sequence Length / Timesteps)：每个样本的序列长度，即该样本的序列长度是多少"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93deabd3",
   "metadata": {},
   "source": [
    "## 二、基础概念\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45aec557",
   "metadata": {},
   "source": [
    "### 2.1 准备工作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a05190b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 准备工作\n",
    "\n",
    "import numpy as np\n",
    "import random\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "# 显示中文字体\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "# 显示负号正常\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# 设置随机种子确保结果可复现，全局随机函数\n",
    "def set_seed(seed=42, deterministic=True):\n",
    "    \"\"\"\n",
    "    设置全局随机种子，确保实验可重复性\n",
    "    \n",
    "    参数:\n",
    "        seed: 随机种子值，默认为42\n",
    "        deterministic: 是否启用确定性模式，默认为True\n",
    "    \"\"\"\n",
    "    # 设置Python的随机种子\n",
    "    random.seed(seed) \n",
    "    os.environ['PYTHONHASHSEED'] = str(seed) # 确保Python哈希函数的随机性一致，比如字典、集合等无序\n",
    "    \n",
    "    # 设置NumPy的随机种子\n",
    "    np.random.seed(seed)\n",
    "    \n",
    "    # 设置PyTorch的随机种子\n",
    "    torch.manual_seed(seed) # 设置CPU上的随机种子\n",
    "    torch.cuda.manual_seed(seed) # 设置GPU上的随机种子\n",
    "    torch.cuda.manual_seed_all(seed)  # 如果使用多GPU\n",
    "    \n",
    "    # 配置cuDNN以确保结果可重复\n",
    "    if deterministic:\n",
    "        torch.backends.cudnn.deterministic = True\n",
    "        torch.backends.cudnn.benchmark = False\n",
    "\n",
    "\n",
    "# 设置随机种子\n",
    "set_seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "238985bf",
   "metadata": {},
   "source": [
    "### 2.2 数据生成"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d07507c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ===== 步骤1：生成合成时间序列 =====\n",
    "x = np.linspace(0, 100, 1000) # 在 0 到 100 之间生成 1000 个均匀分布的点作为x\n",
    "y = np.sin(x) + 0.1 * x + np.random.normal(0, 0.5, 1000)  # 正弦波+线性趋势+噪声\n",
    "# 可视化原始数据\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(y)\n",
    "plt.title('合成时间序列数据（正弦波+趋势+噪声）')\n",
    "plt.xlabel('时间步')\n",
    "plt.ylabel('值')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c34cc488",
   "metadata": {},
   "source": [
    "为了演示序列预测，现在构建了一个近似于sinx的分布，我们后面将要做这个顺序预测，比如用前5个周期预测第六个周期的值。此时这前5个周期就是训练集，第6个周期就是测试集。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "477a5088",
   "metadata": {},
   "source": [
    "### 2.3 单步预测和多步预测概念\n",
    "\n",
    "要注意此时训练集和测试集是有明确的时间顺序的，这是因为预测的任务是有顺序关系的。只有用历史数据来预测未来数据。\n",
    "\n",
    "这个时候我们要明白，具体是如何预测的呢？比如现在有100个数据，80个训练，20个预测\n",
    "\n",
    "过去我们再用机器学习预测结构化的独立同分布的样本的时候，这种预测是训练完模型后，每一个测试集的样本特征输入到模型后就会得到对应的标签。\n",
    "\n",
    "但是现在不同，若没有第 n 个样本的标签，无法预测第 n+1 个样本，因为预测过程需基于历史序列的递推关系（如股票价格预测，需已知前一天价格才能推导出后一天趋势）\n",
    "\n",
    "\n",
    "单步预测（Single-Step Prediction）：一次只预测下一时刻\n",
    "\n",
    "训练集构建：若窗口大小为 5，训练样本形如 [x1,x2,x3,x4,x5]→x6，[x2,x3,x4,x5,x6]→x7，以此类推\n",
    "\n",
    "此时只会预测到81个结束了，单步预测指的是只预测一个时刻，比如只预测未来一天的。\n",
    "\n",
    "\n",
    "多步预测（Multi-Step Prediction）：一次预测多个时刻\n",
    "\n",
    "2种方式\n",
    "- 递归式多步预测（滚动预测）：先用预测第81天的，再用81天的预测数据和历史数据预测第82天的，以此类推，这种方式会造成误差的累计\n",
    "- 直接式多步预测：构建模型直接输出未来多个时刻的值（如输入特征 x60-x80，标签 x81-x90），这样输出的是一次性的预测结果，不会累计误差\n",
    "\n",
    "这种直接式的多步预测，也是我们第一次接触多输入多输出这种情况。我们之前做的回归任务，都是多输入单输出。\n",
    "\n",
    "这种多输入多输出的任务叫做MIMO(Multiple-Input Multiple-Output)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f542c42",
   "metadata": {},
   "source": [
    "### 2.4 多输入多输出任务"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb005c28",
   "metadata": {},
   "source": [
    "其实之前的结构化数据对应的非时序任务都可以完成多输入多少输出任务，比如\n",
    "- 体检指标（血压、血糖、胆固醇）、病史、年龄\n",
    "- 输出：是否患糖尿病（0/1）、冠心病风险（概率值）、肥胖等级（1-5）\n",
    "此时选择什么模型呢？\n",
    "\n",
    "传统的机器学习模型默认单输出，如线性回归、逻辑回归、SVM、决策树等经典算法，原生设计用于单输出任务（即预测一个目标变量）。随机森林貌似是支持多输出任务，但是总归大多不用他们来完成。\n",
    "\n",
    "一般这类任务可以由算法上最为简单的神经网络来实现，有几种思路：\n",
    "\n",
    "1. 把和这个任务拆分成多个任务，每个任务都是多输入单输出，缺点是有可能预测的标签之间可能有某些关系，比如同时预测“价格” 和 “面积” ，这2者可能有关，拆开作为2个机器学习模型分开预测可能丢失这种关系（独立建模）。\n",
    "2. 通过神经网络，构建多输出模型，本质就是修改损失函数为多输出的损失函数（每个目标对应的权重如何分配也是个问题），这其实就是一个联合建模任务，我们在复试班的强化NLP中就提到bert本质就是这么一个联合建模任务，同时考虑到了MLM（掩码语言模型，多分类任务） 和NSP（下一句预测，二分类任务）的损失。\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cfde5cb",
   "metadata": {},
   "source": [
    "这种联合建模的思路，在后面大模型领域，将会不断遇到，自行梳理下我们课上说的知识点的演变逻辑。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec8009ab",
   "metadata": {},
   "source": [
    "## 三、 时序任务实战"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6e94021",
   "metadata": {},
   "source": [
    "### 3.1数据的划分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "06bbda2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ===== 步骤2：数据预处理 =====\n",
    "# 1. 数据标准化\n",
    "scaler = MinMaxScaler(feature_range=(0, 1)) # 创建将数据缩放到0-1范围的缩放器\n",
    "scaled_y = scaler.fit_transform(y.reshape(-1, 1)).flatten() # 将y转换为二维数组并进行缩放，后续再将其展平为一维数组"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79c3a6ba",
   "metadata": {},
   "source": [
    "这里之所以需要转换为二维数组，因为转换器要求输入是二维数组，形状为 [样本数, 特征数]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "80d43a90",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. 划分训练集和测试集（80%训练，20%测试），时间序列任务要按时间顺序划分训练集和测试集\n",
    "train_size = int(len(scaled_y) * 0.8)\n",
    "train_data = scaled_y[:train_size]\n",
    "test_data = scaled_y[train_size:]\n",
    "\n",
    "# 3. 创建时序数据集函数\n",
    "def create_sequences(data, seq_length):\n",
    "    \"\"\"\n",
    "    将数据转换为适合RNN输入的序列格式\n",
    "    参数:\n",
    "        data: 原始时间序列数据\n",
    "        seq_length: 每个输入序列的长度\n",
    "    返回:\n",
    "        X: 输入序列集\n",
    "        y: 目标值集\n",
    "    \"\"\"\n",
    "    X, y = [], [] # 初始化空列表存储输入序列和目标值\n",
    "    for i in range(len(data) - seq_length): # 一共这么多个序列对\n",
    "        X.append(data[i:i+seq_length]) # 截取长度为seq_length的子序列作为列表输入\n",
    "        y.append(data[i+seq_length])  # 对应的下一个值作为目标\n",
    "    return np.array(X), np.array(y) # 转换为NumPy数组返回\n",
    "\n",
    "# 设置序列长度（使用前30个时间步预测下一个）\n",
    "seq_length = 30"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab45beaa",
   "metadata": {},
   "source": [
    "注意上面的代码是先划分训练集和测试集，再对各自数据集应用滑动窗口\n",
    "\n",
    "这样写对么？上面这里的代码本来我是让豆包给我写的，你和他说他错了他还反驳你，难绷。\n",
    "\n",
    "实际上他的处理逻辑是有问题的，这也要求你要自己知道流程，具备逻辑才可以正确处理任务。实际上，正确的做法是，先滑动窗口，再划分训练集和测试集。\n",
    "\n",
    "豆包的做法：原始数据（时间顺序：1→2→…→100），假设滑动窗口是10\n",
    "↓ 按时间划分（前80%训练，后20%测试）\n",
    "训练集：1-80，测试集：81-100\n",
    "↓ 对训练集和测试集分别应用滑动窗口\n",
    "训练集滑动窗口：[1-10]→11，[2-11]→12，…，[71-80]→81\n",
    "测试集滑动窗口：[81-90]→91，[82-91]→92，…，[91-100]→101\n",
    "\n",
    "他这样会出现一个问题，有一段标签81-90这部分的标签没用上，所以在样本缺少的情况下，要先滑动窗口再划分。\n",
    "\n",
    "此外，在正确的测试集中，比如[72-81]→82 是第一个，然后[73-82]→83，那么此时这里的82到底是用的预测的数据，还是真实数据呢？\n",
    "\n",
    "实际上，用的是真实的数据，因为我们做的是单步预测，预测的前提就是知道之前的每一个值，预测明天的值。如果想要预测未来30天的值，应该考虑的是多步预测的2种方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "904b34d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始数据总长度: 1000\n",
      "训练数据原始长度: 800\n",
      "测试数据原始长度: 200\n",
      "------------------------------\n",
      "序列长度 (seq_length): 30\n",
      "滑动窗口后样本总数: 970\n",
      "------------------------------\n",
      "训练集划分点 (split_idx): 770\n",
      "训练集特征(X_train)形状: (770, 30)\n",
      "训练集标签(y_train)形状: (770,)\n",
      "测试集特征(X_test)形状: (200, 30)\n",
      "测试集标签(y_test)形状: (200,)\n",
      "------------------------------\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# # ===== 步骤1：生成合成时间序列  =====\n",
    "# x = np.linspace(0, 100, 1000)\n",
    "# y = np.sin(x) + 0.1 * x + np.random.normal(0, 0.5, 1000)\n",
    "\n",
    "# # =============================================================\n",
    "# # =================== 正确流程的代码实现 ======================\n",
    "# # =============================================================\n",
    "\n",
    "# ===== 步骤2：划分原始数据，并进行正确的标准化 =====\n",
    "\n",
    "# 1. 定义划分点\n",
    "train_size = int(len(y) * 0.8)\n",
    "seq_length = 30\n",
    "\n",
    "# 2. 划分原始数据（仅用于fit缩放器）\n",
    "train_data_raw = y[:train_size]\n",
    "# 注意：测试集暂时不需要单独划分出来\n",
    "\n",
    "# 3. 数据标准化 (关键步骤！)\n",
    "#    - 创建缩放器\n",
    "#    - 仅在训练数据上进行拟合(fit)，学习其分布\n",
    "#    - 对整个数据集进行转换(transform)\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "scaler.fit(train_data_raw.reshape(-1, 1))\n",
    "scaled_y = scaler.transform(y.reshape(-1, 1)).flatten()\n",
    "\n",
    "# ===== 步骤3：对完整的、缩放后的数据应用滑动窗口 =====\n",
    "\n",
    "def create_sequences(data, seq_length):\n",
    "    \"\"\"\n",
    "    将数据转换为适合RNN输入的序列格式 \n",
    "    \"\"\"\n",
    "    X, y = [], []\n",
    "    for i in range(len(data) - seq_length):\n",
    "        X.append(data[i:i+seq_length])\n",
    "        y.append(data[i+seq_length])\n",
    "    return np.array(X), np.array(y)\n",
    "\n",
    "# 在整个数据集上创建序列\n",
    "all_X, all_y = create_sequences(scaled_y, seq_length)\n",
    "\n",
    "# ===== 步骤4：划分序列数据集（X和y） =====\n",
    "\n",
    "# 计算分割点。最后一个训练样本的标签是原始数据中的 train_data[train_size-1]。\n",
    "# 这个样本的起始索引是 (train_size - 1) - seq_length。\n",
    "# 因此，我们总共可以生成 (train_size - seq_length) 个训练样本。\n",
    "split_idx = train_size - seq_length\n",
    "\n",
    "X_train = all_X[:split_idx]\n",
    "y_train = all_y[:split_idx]\n",
    "\n",
    "X_test = all_X[split_idx:]\n",
    "y_test = all_y[split_idx:]\n",
    "\n",
    "# ===== 步骤5：验证结果 =====\n",
    "print(\"原始数据总长度:\", len(y))\n",
    "print(\"训练数据原始长度:\", train_size)\n",
    "print(\"测试数据原始长度:\", len(y) - train_size)\n",
    "print(\"-\" * 30)\n",
    "print(\"序列长度 (seq_length):\", seq_length)\n",
    "print(\"滑动窗口后样本总数:\", len(all_X))\n",
    "print(\"-\" * 30)\n",
    "print(\"训练集划分点 (split_idx):\", split_idx)\n",
    "print(\"训练集特征(X_train)形状:\", X_train.shape) # (770, 30) -> (800-30, 30)\n",
    "print(\"训练集标签(y_train)形状:\", y_train.shape)   # (770,)\n",
    "print(\"测试集特征(X_test)形状:\", X_test.shape)   # (200, 30) -> (1000-30 - 770, 30)\n",
    "print(\"测试集标签(y_test)形状:\", y_test.shape)     # (200,)\n",
    "print(\"-\" * 30)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43d119fc",
   "metadata": {},
   "source": [
    "### 3.2 模型搭建"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1c327e7",
   "metadata": {},
   "source": [
    "此时有一个问题存在，我们的数据已经整理成了x-y这种形式对，那么他可以借助经典机器学习模型来预测么？\n",
    "\n",
    "本质就是是否可以把这个任务理解为I.I.D. 数据集，也就是数据之间相互独立且同分布。\n",
    "\n",
    "思考下，此时划分数据集的时候，按照了时间顺序划分，所以训练集看不到未来的信息，这里没问题。然后训练的时候，是否可以打乱训练集样本的顺序呢？\n",
    "\n",
    "\n",
    "答案是可以的，把每一个样本都看成是一个完整的因果故事（特征--标签），原因如下：\n",
    "\n",
    "1. 虽然训练集中如果修改样本顺序，会造成可能会看到前一个样本的标签，但是深度学习在梯度更新，是每个批次中每个样本的损失都计算完之后，取平均方向进行更新，所以每个样本单独的损失计算，此时权重还没更新，在同一轮训练中看不到其他样本的信息。（注意是同一轮训练中）\n",
    "2. 打乱有利于每次的相邻的几组batch的梯度出现差异，比如一个长期上升的趋势会导致梯度更新一致的方向，这可能让模型陷入局部最优。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb3ceb9b",
   "metadata": {},
   "source": [
    "聪明的你发现了，这个x-y的标签对 完全可以用经典机器学习模型来解决，比如我们下面用随机森林来解决。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ea1287d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "为随机森林准备的 X_train 形状: (770, 30)\n",
      "为随机森林准备的 y_train 形状: (770,)\n",
      "为随机森林准备的 X_test 形状: (200, 30)\n",
      "\n",
      "开始训练随机森林模型...\n",
      "模型训练完成！\n",
      "\n",
      "训练集 RMSE: 0.2370\n",
      "测试集 RMSE: 1.4416\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# =============================================================\n",
    "# ===== 步骤1：数据准备 (与之前完全相同) =====\n",
    "# =============================================================\n",
    "\n",
    "# 生成合成时间序列\n",
    "x = np.linspace(0, 100, 1000)\n",
    "y = np.sin(x) + 0.1 * x + np.random.normal(0, 0.5, 1000)\n",
    "\n",
    "# 定义参数\n",
    "train_size = int(len(y) * 0.8)\n",
    "seq_length = 30\n",
    "\n",
    "# 正确的数据标准化\n",
    "train_data_raw = y[:train_size]\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "scaler.fit(train_data_raw.reshape(-1, 1))\n",
    "scaled_y = scaler.transform(y.reshape(-1, 1)).flatten()\n",
    "\n",
    "# 创建时序数据集函数\n",
    "def create_sequences(data, seq_length):\n",
    "    X, y = [], []\n",
    "    for i in range(len(data) - seq_length):\n",
    "        X.append(data[i:i+seq_length])\n",
    "        y.append(data[i+seq_length])\n",
    "    return np.array(X), np.array(y)\n",
    "\n",
    "# 对完整数据应用滑动窗口\n",
    "all_X, all_y = create_sequences(scaled_y, seq_length)\n",
    "\n",
    "# 划分序列数据集\n",
    "split_idx = train_size - seq_length\n",
    "X_train_np = all_X[:split_idx]\n",
    "y_train_np = all_y[:split_idx]\n",
    "X_test_np = all_X[split_idx:]\n",
    "y_test_np = all_y[split_idx:]\n",
    "\n",
    "# =========================================================================\n",
    "# ===== 步骤2：为机器学习模型准备数据 (关键区别点!) =====\n",
    "# =========================================================================\n",
    "\n",
    "# 1. 调整X的形状\n",
    "# Scikit-learn的机器学习模型需要二维的输入: [样本数, 特征数]\n",
    "# RNN需要的是三维输入: [样本数, 时间步长, 特征数]\n",
    "# 我们需要将每个样本的 `seq_length` 个时间步“扁平化”成 `seq_length` 个特征。\n",
    "# 原始形状: (770, 30, 1) or (770, 30) -> 目标形状: (770, 30)\n",
    "\n",
    "# 获取样本数\n",
    "n_samples_train = X_train_np.shape[0]\n",
    "n_samples_test = X_test_np.shape[0]\n",
    "\n",
    "# 将三维或二维的X reshape为二维\n",
    "X_train_rf = X_train_np.reshape(n_samples_train, -1)\n",
    "X_test_rf = X_test_np.reshape(n_samples_test, -1)\n",
    "\n",
    "# y_train_np 和 y_test_np 已经是 (n_samples,) 的一维数组，可以直接使用。\n",
    "\n",
    "print(\"为随机森林准备的 X_train 形状:\", X_train_rf.shape) # (770, 30)\n",
    "print(\"为随机森林准备的 y_train 形状:\", y_train_np.shape)   # (770,)\n",
    "print(\"为随机森林准备的 X_test 形状:\", X_test_rf.shape)    # (200, 30)\n",
    "\n",
    "# 注意：我们不再需要 PyTorch 的 Tensor, TensorDataset 和 DataLoader\n",
    "\n",
    "# =============================================================\n",
    "# ===== 步骤3：创建、训练和评估随机森林模型 =====\n",
    "# =============================================================\n",
    "\n",
    "# 1. 创建随机森林回归模型\n",
    "# n_estimators: 森林中树的数量\n",
    "# random_state: 保证每次运行结果一致，便于复现\n",
    "# n_jobs=-1: 使用所有可用的CPU核心进行并行计算，加快训练速度\n",
    "rf_model = RandomForestRegressor(n_estimators=100, random_state=42, n_jobs=-1)\n",
    "\n",
    "# 2. 训练模型\n",
    "print(\"\\n开始训练随机森林模型...\")\n",
    "rf_model.fit(X_train_rf, y_train_np)\n",
    "print(\"模型训练完成！\")\n",
    "\n",
    "# 3. 做出预测\n",
    "train_predict = rf_model.predict(X_train_rf)\n",
    "test_predict = rf_model.predict(X_test_rf)\n",
    "\n",
    "# 4. 反标准化预测结果，以便在原始尺度上进行比较\n",
    "# scaler.inverse_transform 需要二维输入，所以先 reshape\n",
    "train_predict = scaler.inverse_transform(train_predict.reshape(-1, 1))\n",
    "test_predict = scaler.inverse_transform(test_predict.reshape(-1, 1))\n",
    "\n",
    "# 原始标签也需要反标准化\n",
    "y_train_orig = scaler.inverse_transform(y_train_np.reshape(-1, 1))\n",
    "y_test_orig = scaler.inverse_transform(y_test_np.reshape(-1, 1))\n",
    "\n",
    "# 5. 计算均方根误差 (RMSE)\n",
    "train_rmse = np.sqrt(mean_squared_error(y_train_orig, train_predict))\n",
    "test_rmse = np.sqrt(mean_squared_error(y_test_orig, test_predict))\n",
    "print(f\"\\n训练集 RMSE: {train_rmse:.4f}\")\n",
    "print(f\"测试集 RMSE: {test_rmse:.4f}\")\n",
    "\n",
    "\n",
    "# =============================================================\n",
    "# ===== 步骤4：可视化结果 =====\n",
    "# =============================================================\n",
    "\n",
    "plt.figure(figsize=(15, 7))\n",
    "plt.plot(y, label='原始数据', color='gray', alpha=0.5)\n",
    "\n",
    "# 绘制训练集的预测结果\n",
    "train_predict_plot = np.empty_like(y)\n",
    "train_predict_plot[:] = np.nan\n",
    "train_predict_plot[seq_length : len(train_predict) + seq_length] = train_predict.flatten()\n",
    "plt.plot(train_predict_plot, label='训练集预测值 (RF)', color='blue')\n",
    "\n",
    "# 绘制测试集的预测结果\n",
    "test_predict_plot = np.empty_like(y)\n",
    "test_predict_plot[:] = np.nan\n",
    "test_predict_plot[len(train_predict) + seq_length : len(y)] = test_predict.flatten()\n",
    "plt.plot(test_predict_plot, label='测试集预测值 (RF)', color='red')\n",
    "\n",
    "plt.title('时间序列预测结果对比 (随机森林)')\n",
    "plt.xlabel('时间步')\n",
    "plt.ylabel('值')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b28af785",
   "metadata": {},
   "source": [
    "可以看到结果非常差\n",
    "\n",
    "训练集表现 (蓝色部分): 完美拟合，近乎过拟合：在时间步0到800的训练区间内，蓝色的预测线几乎完美地覆盖了灰色的原始数据线。这说明随机森林模型非常强大，它有足够的能力去“背诵”或“记忆”训练数据。它学习到了在训练集范围内，什么样的输入窗口（X_train）对应什么样的输出值（y_train）。\n",
    "\n",
    "测试集表现 (红色部分): 完全失败。当进入时间步800以后的测试集时，情况急转直下。初始阶段: 在测试集刚开始的一小段，模型似乎还能跟上，因为测试集初期的输入窗口和训练集末期的窗口非常相似。灾难性平线: 很快，模型的预测就变成了一条几乎水平的直线，完全忽略了原始数据仍在持续的上升趋势和周期性波动。\n",
    "\n",
    "\n",
    "\n",
    "这个结果堪称教科书级别的案例，它完美地揭示了随机森林（以及所有基于决策树的模型）在处理带有趋势的时间序列数据时的根本性弱点。\n",
    "\n",
    "\n",
    "这个失败的根源在于随机森林（或任何决策树模型）的工作原理：\n",
    "\n",
    "决策树模型无法外推 (Cannot Extrapolate)。一个决策树通过一系列“是/否”问题来对数据进行划分。一个输入样本会顺着树的分支一直走到某个叶子节点。这个叶子节点的预测值，是所有在训练过程中落入这个叶子节点的训练样本标签(y)的平均值。随机森林是很多棵决策树的集合。它的最终预测值是所有树预测值的平均值。\n",
    "\n",
    "这意味着，无论是单棵树还是整个森林，其最终的预测结果永远不可能超过它在训练集中见到过的最大目标值(y)，也不可能低于它见到过的最小目标值(y)。也就是说随机森林模型没有学到“趋势”这个抽象概念。它只学到了一个静态的映射：当输入在某个范围内时，输出就在某个范围内。当测试集的输入和输出都超出了它见过的范围时，它就只能给出它知识边界内的最大值，从而形成了一条无力的平线。\n",
    "\n",
    "那么如何解决这个问题呢？我们下一次内容来提及"
   ]
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